Introduction

In this article we analyze the territorial income distribution in the city of Barcelona using data obtained from Barcelona open data repositorie: https://opendata-ajuntament.barcelona.cat/data/en/dataset/est-renda-familiar

import folium
import pandas as pd
import numpy as np

renta = pd.read_csv("2017_distribucio_territorial_renda_familiar.csv")
renta['NOM'] = renta['Nom_Barri']
renta['NOM'][renta['NOM']=='el Poble Sec'] = 'el Poble-sec'
renta

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM
0	2017	1	Ciutat Vella	1	el Raval	47986	71.2	el Raval
1	2017	1	Ciutat Vella	2	el Barri Gòtic	16240	106.1	el Barri Gòtic
2	2017	1	Ciutat Vella	3	la Barceloneta	15101	79.6	la Barceloneta
3	2017	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	22923	99.4	Sant Pere, Santa Caterina i la Ribera
4	2017	2	Eixample	5	el Fort Pienc	32048	106.5	el Fort Pienc
...	...	...	...	...	...	...	...	...
68	2017	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	13710	150.1	Diagonal Mar i el Front Marítim del Poblenou
69	2017	10	Sant Martí	70	el Besòs i el Maresme	22893	60.4	el Besòs i el Maresme
70	2017	10	Sant Martí	71	Provençals del Poblenou	20649	102.3	Provençals del Poblenou
71	2017	10	Sant Martí	72	Sant Martí de Provençals	26187	67.4	Sant Martí de Provençals
72	2017	10	Sant Martí	73	la Verneda i la Pau	28725	57.0	la Verneda i la Pau

73 rows × 8 columns

https://opendata-ajuntament.barcelona.cat/data/en/dataset/20170706-districtes-barris provides json files with detail of administrative units of the city of Barcelona : districts, neighbourhoods, area of interest, basic statistical areas (AEB) and census areas. Today, Barcelona contains 10 different districts and 73 neighborhoods. In this article we employ neighborhood datas.

import json

with open('0301100100_UNITATS_ADM_POLIGONS.json', 'r') as f:
    data_o = json.load(f) 
data_o['features'] = data_o['features'][11:84]
for n in range(73):
    data['features'][n]['properties']['Índex RFD Barcelona = 100']= renta['Índex RFD Barcelona = 100'].iloc[n]

According to https://opendata-ajuntament.barcelona.cat/ca/faqs , the coordinates in this json file use Spatial Reference System EPSG 25831, we need to transfom this system into EPSG 4326, which is the more standard reference system that we use nowadays. The simplest way to transform coordinates in Python is pyproj, i.e. the Python interface to PROJ.4 library.

from pyproj import Transformer

def trans(coordinate):
    transformer = Transformer.from_crs("epsg:25831", "epsg:4326")
    x2,y2 = transformer.transform(coordinate[0],coordinate[1])
    return [y2, x2]

for n in range(73):
    for i in range(len(data['features'][n]['geometry']['coordinates'][0])):
        data['features'][n]['geometry']['coordinates'][0][i] = trans(data['features'][n]['geometry']['coordinates'][0][i])
    if len(data['features'][n]['geometry']['coordinates'])>1:
        for j in range(len(data['features'][n]['geometry']['coordinates'][1])):
            data['features'][n]['geometry']['coordinates'][1][j] = trans(data['features'][n]['geometry']['coordinates'][1][j])
    print(n)

import json
from copy import deepcopy

with open('bcn_neighborhood.json', 'r') as f:
    data = json.load(f)

for n in range(73):
    data['features'][n]['properties']['Índex RFD Barcelona = 100']= renta['Índex RFD Barcelona = 100'].iloc[n]

data1 = deepcopy(data)
data2 = deepcopy(data)

len(data1['features'])

73

len(data2['features'])

73

index1=0
index2=0
while index1< len(data1['features']):
    if data1['features'][index1]['properties']['Índex RFD Barcelona = 100']>=100:
        data1['features'].pop(index1)
    else: index1+=1

while index2< len(data2['features']):
    if data2['features'][index2]['properties']['Índex RFD Barcelona = 100']<100:
        data2['features'].pop(index2)
    else: index2+=1

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta,   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook

BCN_map.save("bcn_renta_2017.html")

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data1, # GeoJson Coordinates
                   data= renta[renta['Índex RFD Barcelona = 100']<100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='PuRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))

choropleth = folium.Choropleth(geo_data=data2, # GeoJson Coordinates
                   data=renta[renta['Índex RFD Barcelona = 100']>=100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlGn',
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook

import branca.colormap as cm

barcelona_map = folium.Map([41.40,2.17], zoom_start=12) # create a folium map
folium.GeoJson(data, style_function= lambda feature: {
        'fillColor': '#ff0000' if feature['properties']['Índex RFD Barcelona = 100'] < 100 else '#008000'
    }).add_to(barcelona_map)
barcelona_map

colorscale = cm.LinearColormap(colors=['red', 'green'], index=[0, 1])
barcelona_map.add_child(colorscale)

Make this Notebook Trusted to load map: File -> Trust Notebook

renta_07 = pd.read_csv("2007_distribucio_territorial_renda_familiar.csv")
renta_07 = renta_07.iloc[:-1]
renta_07['NOM'] = renta_07['Nom_Barri']
renta_07['NOM'][renta_07['NOM']=='el Poble Sec'] = 'el Poble-sec'
renta_07['Índex RFD Barcelona = 100'] = renta_07['Índex RFD Barcelona = 100'].astype('float64')
renta_07['RFD change'] = (renta['Índex RFD Barcelona = 100']-renta_07['Índex RFD Barcelona = 100'])/renta_07['Índex RFD Barcelona = 100']*100
renta_07

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_4851/2225712002.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  renta_07['NOM'][renta_07['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM	RFD change
0	2007	1	Ciutat Vella	1	el Raval	46595	64.7	el Raval	10.046368
1	2007	1	Ciutat Vella	2	el Barri Gòtic	27946	86.5	el Barri Gòtic	22.658960
2	2007	1	Ciutat Vella	3	la Barceloneta	15921	66.7	la Barceloneta	19.340330
3	2007	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	22572	80.2	Sant Pere, Santa Caterina i la Ribera	23.940150
4	2007	2	Eixample	5	el Fort Pienc	31521	107.9	el Fort Pienc	-1.297498
...	...	...	...	...	...	...	...	...	...
68	2007	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	9775	101.1	Diagonal Mar i el Front Marítim del Poblenou	48.466864
69	2007	10	Sant Martí	70	el Besòs i el Maresme	22652	61.7	el Besòs i el Maresme	-2.106969
70	2007	10	Sant Martí	71	Provençals del Poblenou	18731	85.7	Provençals del Poblenou	19.369895
71	2007	10	Sant Martí	72	Sant Martí de Provençals	26261	81.5	Sant Martí de Provençals	-17.300613
72	2007	10	Sant Martí	73	la Verneda i la Pau	29452	74.8	la Verneda i la Pau	-23.796791

73 rows × 9 columns

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_07,   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook

type(data['features'][n]['properties']['Població'])

numpy.int64

for n in range(73):
    data['features'][n]['properties']['Població']= int(renta_07['Població'].iloc[n])
    
BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_07,   # The table which contains the values we are analysing
                   columns=['NOM', 'Població'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN population').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Població'], 
aliases=['Neiboorhood','Population']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook

for n in range(73):
    data['features'][n]['properties']['RFD change']= renta_07['RFD change'].iloc[n]
    
BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_07,   # The table which contains the values we are analysing
                   columns=['NOM', 'RFD change'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'RFD change'], 
aliases=['Neiboorhood','RFD change %']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook

data3 = deepcopy(data)
data4 = deepcopy(data)
index1=0
index2=0

while index1< len(data3['features']):
    if data3['features'][index1]['properties']['RFD change']>=0:
        data3['features'].pop(index1)
    else: index1+=1

while index2< len(data4['features']):
    if data4['features'][index2]['properties']['RFD change']<0:
        data4['features'].pop(index2)
    else: index2+=1

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data3, # GeoJson Coordinates
                   data=renta_07[renta_07['RFD change']<0],   # The table which contains the values we are analysing
                   columns=['NOM', 'RFD change'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='PuRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'RFD change'], 
aliases=['Neiboorhood','RFD change %']))
choropleth = folium.Choropleth(geo_data=data4, # GeoJson Coordinates
                   data=renta_07[renta_07['RFD change']>=0],   # The table which contains the values we are analysing
                   columns=['NOM', 'RFD change'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlGn',
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'RFD change'], 
aliases=['Neiboorhood','RFD change %']))
BCN_map

Make this Notebook Trusted to load map: File -> Trust Notebook